Doctors in hospitals manually inspect a large number of x-ray images for
fractures. Manual inspection
is tedious and time consuming. A tired radiologist
has been found to miss a fractured image among many healthy ones. Computer vision
system can help to screen x-ray images for suspicious cases and alarm the doctors. 
Fractures at femur (thight), radius (wrist), and chest.
Developing a prototype system for field test in local hospital.
International patent application published under Patent
Cooperation Treaty (PCT):
Method and System for Detection of Bone Fractures, International Publication Number WO
2005/083635 A1, 9 Sep 2005.
US patent filed.
Method and System for
Detection of Bone Fractures. US 2007/0274584, filed 29 Nov 2007.
1. Compute Neck-Shaft Angle
accuracy:
93.5%, fracture detection rate: 61.5%
Femurs
correctly identified as healthy:
Femurs correctly identified as fractured:
accuracy:
93.5%, fracture detection rate: 46.2%Gabor texture orientation maps of correctly identified healthy femurs:
Gabor texture orientation maps of correctly identified fractured femurs:
Combined performance of methods 1 and 2:
accuracy: 95.4%, fracture detection rate: 76.9%
3. Analyze Intesity Gradient Map
color circle indicating gradient direction
Intensity gradient map of correctly identified healthy femurs:
Intensity gradient map of correctly identified fractured femur:
Combined performance of all methods:
accuracy: 98.2%, fracture detection rate: 92.2%
4. Combination of Various Features and Classifiers:
Combine neck-shaft angle measurement, Gabor texture orientation map, intensity gradient map,
and Markon Random Field with various classifiers. So far, Gini-SVM seems to perform better than
other classifiers that we have tested. The combined method can detect very subtle fractures:
Performance:
Max rule: accuracy: 98.1%, fracture detection rate: 91.7%
1-of-3 rule: accuracy: 97.2%, fracture detection rate: 100%
Can detect radius features:
Performance:
Max rule: accuracy: 95.9%, fracture detection rate: 95.7%
1-of-3 rule: accuracy: 85.1%, fracture detection rate: 100%
A/Prof. Leow Wee Kheng,
Dept. of Computer Science, National University of Singapore. Dr.
Howe Tet Sen, Dept. of Orthopaedics, Singapore General Hospital.
Dr. Png Meng Ai, Dept.
of Diagnostic Radiology, Singapore General Hospital. Ms. Ee Xian He (M.Sc., RA), Dept. of Computer Science, National University of Singapore.Ex-students:
Mr. Chen Ying (M.Sc., RA), Dept. of Computer Science, National University of Singapore. Ms. Vineta Lum Lai Fun (Honours), Dept. of Computer Science, National University of Singapore. Mr. Dennis Lim Sher Ee (Honours), Dept. of Computer Science, National University of Singapore. Ms. Ee Xian He (Honours), Dept. of Computer Science, National University of Singapore.
Mr. Tian Tai Peng (M.Sc.),
Dept. of Computer Science, National University of Singapore.
Mr. Dennis Yap Wen-Hsiang
(Honours), Dept. of Computer Science, National University of Singapore.
Mr. William Sze Wing
Kay (Honours), Dept. of Elec. & Comp. Eng., National University of Singapore.
Ms. Toh Beng Keow (Honours),
Dept. of Elec. & Comp. Eng., National University of Singapore.
F. Ding, W. K. Leow, and T. S. Howe. Automatic Segmentation of Femur Bones in Anterior-PosteriorPelvis X-ray Images. In Proc. Int. Conf. on Computer Analysis of Images and Patterns, 2007, pp. 205-
212.
J. Congfu He, W. K. Leow, and T. S. Howe. Hierarchical Classifiers for Detection of Fractures in X-RayImages. In Proc. Int. Conf. on Computer Analysis of Images and Patterns, 2007, pp. 962-969.
Y. Chen, X. Ee, W. K. Leow, T. S. Howe. Automatic Extraction of Femur Contours from Hip X-rayImages. In Proc. First International Workshop on Computer Vision for Biomedical Image Applications
(CVBIA 2005) (in conjunction with Int. Conf. on Computer Vision, 2005). Y. Liu, T. Jiang, C. Zhang
(Eds.), LNCS 3765, Springer, 2005, pp. 200-209. .
Y. Chen,
Model-Based Approach for Extracting Femur Contours in X-ray Images, M.Sc. Thesis, Dept. of
Computer Science, School of Computing, National University of Singapore, 2005.
V. L. F. Lum, W. K. Leow, Y. Chen, T. S. Howe, M. A. Png. Combining Classifiers for Bone Fracture
Detection in X-Ray Images. In Proc. Int. Conf. on Image Processing, 2005.
S. E. Lim, Y. Xing, Y. Chen, W. K. Leow, T. S. Howe, and M. A. Png. Detection of Femur and Radius
Fractures in X-Ray Images. In Proc. 2nd Int. Conf. on Advances in Medical Signal and Information
Processing, 2004, p. 249-256.
D. W.-H. Yap, Y. Chen, W. K. Leow,
T. S. Howe, and M. A. Png. Detecting Femur Fractures by
Texture
Analysis of Trabeculae. In Proc. Int. Conf. on Pattern Recognition, 2004, vol. 3, p. 730-733.
T. P. Tian, Y. Chen, W. K. Leow, W. Hsu, T. S. Howe, M. A. Png. Computing
neck-shaft angle of femur
for x-ray fracture detection. In Proc. Int. Conf.
on Computer Analysis of Images and Patterns, LNCS
2756, 2003, p. 82-89.
T. P. Tian, Detection of Femur Fractures in X-Ray Images, M.Sc. Thesis, Dept. of Computer Science,
School of Computing, National University of Singapore, 2002.
